CN113327660A

CN113327660A - Motion data processing method and device

Info

Publication number: CN113327660A
Application number: CN202010131304.7A
Authority: CN
Inventors: 王俊岭
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-02-28
Filing date: 2020-02-28
Publication date: 2021-08-31

Abstract

According to the method and the device for processing the motion data, the motion data of the user are obtained firstly, and the motion trail of the user in the motion process and the motion curve of the user with the motion stimulation degree in the motion process are determined according to the motion data; and displaying the motion curve. It can be seen that, different from the prior art, when displaying the motion record to the user, the motion curve for indicating the motion trajectory and the motion stimulation degree in the motion process of the user is determined first, so that when performing visual display, the motion trajectory in the motion process is displayed to the user, the motion stimulation degree in the motion process is also displayed to the user, the display content is enriched, and the user experience is improved.

Description

Motion data processing method and device

Technical Field

The present application relates to the field of terminal technologies, and in particular, to a method and an apparatus for processing motion data.

Background

With the continuous development of sensor technology and artificial intelligence technology, wearable devices play an increasingly important role in daily life. The intelligent perception technology and the visualization technology are two important technologies of wearable equipment. Smart perception technology utilizes the ability of wearable devices to provide smart perception and smart judgment, such as wear detection technology, gesture recognition technology, and the like. Visualization technology is the ultimate value presentation medium for wearable devices to interact with users, e.g., running track recording, swimming track recording, etc.

When the user's motion trail record is displayed to the user by using the visualization technology, taking running as an example, the user can acquire the coordinates of the user at different moments by using a Global Positioning System (GPS) technology in the running process, and perform curve fitting on the coordinate points at different moments by using a curve fitting technology to obtain the motion trail curve of the user in the whole running process, and the user is displayed with the motion trail curve by combining with a map engine, the display content is single, so that the user's experience is not good.

Disclosure of Invention

The embodiment of the application provides a method and a device for processing motion data, which enrich display contents and improve user experience.

In a first aspect, an embodiment of the present application provides a method for processing motion data, where the method for processing motion data may include:

motion data of a user is acquired.

Determining a motion curve of the user according to the motion data; the motion curve is used for indicating the motion trail of the user in the motion process and the motion stimulation degree in the motion process.

The motion curve is displayed.

Therefore, the motion data processing method provided by the embodiment of the application obtains the motion data of the user, and determines the motion trail and the motion stimulation degree of the user in the motion process according to the motion data; and displaying the motion curve. It can be seen that, different from the prior art, when displaying the motion record to the user, the motion curve for indicating the motion trajectory and the motion stimulation degree in the motion process of the user is determined first, so that when performing visual display, the motion trajectory in the motion process is displayed to the user, the motion stimulation degree in the motion process is also displayed to the user, the display content is enriched, and the user experience is improved.

In one possible implementation, determining a motion curve of the user according to the motion data may include:

respectively determining a motion track curve and a motion stimulation degree curve according to the motion data; the motion trail curve is used for representing a motion trail of a user in a motion process, and the motion stimulation degree curve is used for representing a corresponding relation between time and motion stimulation degree in the motion process.

And determining a motion curve according to the motion track curve and the motion stimulation degree curve. Therefore, when the visual display is carried out, the movement track in the movement process is displayed for the user, the movement stimulation degree in the movement process is also displayed for the user, the display content is enriched, and the user experience is improved.

In one possible implementation, determining the motion curve according to the motion trajectory curve and the motion stimulation degree curve may include:

and determining colors corresponding to the exercise stimulation degrees of at least two first time points in the exercise stimulation degree curve according to the mapping relation between the exercise stimulation degrees and the colors.

In the motion curve, the color corresponding to each first time point in at least two first time points is modified into the color corresponding to the motion stimulation degree of the first time point, so that the motion curve is obtained, when visual display is carried out, the motion curve in the motion process is displayed for a user, the motion stimulation degree in the motion process can be visually displayed for the user through the color of the motion curve, the display content is enriched, and the user experience is improved.

In one possible implementation, determining a motion trajectory curve according to the motion data may include:

and determining the motion item corresponding to the motion data based on a big data analysis technology.

And determining a motion trail curve according to the motion item.

In one possible implementation, determining a motion trajectory curve according to the motion item may include:

if the motion trail corresponding to the motion item belongs to the first type, determining a preset motion trail curve corresponding to the motion item as a motion trail curve; and the motion items belonging to the first type correspond to respective motion trail curves.

If the motion trail corresponding to the motion item belongs to the second type, fitting the motion data by adopting a curve fitting technology to obtain a motion trail curve; wherein the complexity of the motion trajectory of the motion item belonging to the second type is higher than the complexity of the motion trajectory of the motion item belonging to the first type.

It can be seen that, in the embodiment of the present application, for a motion item having a simple motion trajectory with a low complexity, when determining a corresponding motion trajectory curve, a motion trajectory curve corresponding to the motion item may be obtained without calculating according to currently obtained motion data, but a preset motion trajectory curve corresponding to the motion item is read, and the preset motion trajectory curve corresponding to the motion item is determined as the motion trajectory curve corresponding to the motion item, so that not only the complexity of terminal calculation may be reduced, but also the motion trajectory may be optimized, and the accuracy of the motion trajectory curve is improved. And for the motion trail with higher complexity, fitting the motion data by adopting a curve fitting technology to obtain a motion trail curve.

In one possible implementation, the motion data includes at least one of a motion speed, a motion acceleration, a motion altitude, or a heart rate, and determining the motion stimulation level curve according to the motion data may include:

and determining the exercise stimulation degree of each second time point according to the exercise data of each second time point in at least two time points in the exercise process.

The motion stimulation degrees of at least two time points are fitted by adopting a polynomial fitting technology to obtain a motion stimulation degree curve, so that the motion stimulation degree curve can be superposed on the motion trail curve to be displayed when visual display is carried out subsequently, the motion trail in the motion process is displayed for a user, the motion stimulation degree in the motion process is also displayed for the user, the display content is enriched, and the user experience is improved.

In one possible implementation manner, the motion data includes a motion speed, a motion acceleration, a motion altitude, and a heart rate, and determining the motion stimulation degree at each of at least two time points during the motion process according to the motion data at the second time point may include:

and respectively carrying out normalization processing on the movement speed, the movement acceleration, the movement height and the heart rate corresponding to the second time point to obtain the processed movement speed, movement acceleration, movement height and heart rate.

And determining the motion stimulation degree of the second time point according to the processed motion speed, motion acceleration, motion height and heart rate.

In a possible implementation manner, the method for processing motion data may further include:

displaying key information in the motion process; wherein the key information comprises at least one of a maximum movement height, a maximum movement speed, a maximum heart rate, or a movement stimulation level score. Therefore, the movement track of the user in the movement process and the movement stimulation degree of the user in the movement process can be displayed for the user, and the display content can be further enriched by simultaneously displaying the key information to the user, so that the user experience is improved.

and (4) integrating the exercise stimulation degree curve to obtain an integration result.

Determining a motor stimulation degree score according to the ratio of the integration result to the duration time of the motor process; the duration of the exercise process is determined according to the ending time and the starting time of the exercise process, so that after the exercise stimulation degree score is obtained, the exercise stimulation degree score can be displayed to the user together, display content is enriched, and user experience is improved.

In one possible implementation, determining the exercise stimulation degree score according to a ratio of the integration result to the duration of the exercise process may include:

and normalizing the value to obtain a normalized ratio.

And processing the normalized ratio based on a preset scoring interval to obtain a motor stimulation degree score, wherein the obtained motor stimulation degree score falls into the preset scoring interval.

In one possible implementation manner, the motion data includes a motion speed and a motion altitude, and the processing method of the motion data may further include:

if the movement speed is greater than the first speed threshold and/or the movement height is greater than the first height threshold, it is determined that the user enters the movement state. If the movement speed is smaller than the second speed threshold and the movement height is smaller than the second height threshold, the user is determined to exit from the movement state, whether the user enters the movement state or not can be judged through the movement speed and/or the movement height, whether the user exits from the movement state or not can be judged through the movement speed and the movement height, the user does not need to manually click to enter the movement state or manually click to exit from the movement state, and automatic control is achieved.

In a second aspect, an embodiment of the present application further provides a device for processing motion data, where the device for processing motion data may include:

an acquisition unit for acquiring motion data of a user.

The processing unit is used for determining a motion curve of the user according to the motion data; the motion curve is used for indicating the motion trail of the user in the motion process and the motion stimulation degree in the motion process.

And the display unit is used for displaying the motion curve.

In a possible implementation manner, the processing unit is used for respectively determining a motion track curve and a motion stimulation degree curve according to the motion data; determining a motion curve according to the motion trail curve and the motion stimulation degree curve; the motion trail curve is used for representing a motion trail of a user in a motion process, and the motion stimulation degree curve is used for representing a corresponding relation between time and motion stimulation degree in the motion process.

In a possible implementation manner, the processing unit is configured to determine, according to a mapping relationship between the exercise stimulation degrees and the colors, colors corresponding to the exercise stimulation degrees at least two first time points in the exercise stimulation degree curve; and modifying the color corresponding to each of the at least two first time points in the motion trail curve into the color corresponding to the motion stimulation degree of the first time point to obtain the motion curve.

In a possible implementation manner, the processing unit is specifically configured to determine, based on a big data analysis technology, an exercise item corresponding to the exercise data; and determining a motion trail curve according to the motion items.

In a possible implementation manner, the processing unit is specifically configured to determine, if a motion trajectory corresponding to the motion item belongs to a first type, a preset motion trajectory curve corresponding to the motion item as a motion trajectory curve; the motion items belonging to the first type correspond to respective motion trail curves; if the motion trail corresponding to the motion item belongs to the second type, fitting the motion data by adopting a curve fitting technology to obtain a motion trail curve; wherein the complexity of the motion trajectory of the motion item belonging to the second type is higher than the complexity of the motion trajectory of the motion item belonging to the first type.

In one possible implementation, the motion data includes at least one of a motion speed, a motion acceleration, a motion altitude, or a heart rate.

The processing unit is specifically used for determining the exercise stimulation degree of each second time point according to the exercise data of each second time point in at least two time points in the exercise process; and fitting the exercise stimulation degrees of at least two time points by adopting a polynomial fitting technology to obtain an exercise stimulation degree curve.

In one possible implementation, the motion data includes motion speed, motion acceleration, motion altitude, and heart rate.

The processing unit is specifically used for respectively carrying out normalization processing on the movement speed, the movement acceleration, the movement height and the heart rate corresponding to the second time point to obtain the processed movement speed, movement acceleration, movement height and heart rate; and determining the exercise stimulation degree of the second time point according to the processed exercise speed, the exercise acceleration, the exercise height and the heart rate.

In a possible implementation manner, the display unit is further used for displaying key information in the motion process; wherein the key information comprises at least one of a maximum movement height, a maximum movement speed, a maximum heart rate, or a movement stimulation level score.

In a possible implementation manner, the processing unit is further configured to integrate the exercise stimulation degree curve to obtain an integration result; determining a motor stimulation degree score according to the ratio of the integral result to the duration time of the motor process; wherein the duration of the movement process is determined according to the ending time and the starting time of the movement process.

In a possible implementation manner, the processing unit is specifically configured to normalize the ratio to obtain a normalized ratio; and processing the normalized ratio based on a preset scoring interval to obtain a motor stimulation degree score.

In a possible implementation manner, the exercise data includes an exercise speed and an exercise height, and the processing unit is further configured to determine that the user enters the exercise state if the exercise speed is greater than a first speed threshold and/or the exercise height is greater than a first height threshold; and if the movement speed is less than the second speed threshold value and the movement height is less than the second height threshold value, determining that the user exits the movement state.

In a third aspect, an embodiment of the present application further provides a communication apparatus, where the apparatus includes a processor and a memory, where the memory stores a computer program, and the processor executes the computer program stored in the memory, so as to enable the apparatus to perform the method for processing motion data as described in any one of the foregoing possible implementation manners of the first aspect.

In a fourth aspect, an embodiment of the present application further provides a communication apparatus, including: a processor and interface circuitry.

The interface circuit is used for receiving code instructions and transmitting the code instructions to the processor.

The processor is configured to execute the code instructions to perform the method for processing the motion data according to any one of the foregoing possible implementation manners of the first aspect.

In a fifth aspect, the present application further provides a readable storage medium, which stores instructions that, when executed, cause the method for processing motion data according to any one of the foregoing possible implementations of the first aspect.

Drawings

Fig. 1 is a schematic diagram of a possible application scenario provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of a method for processing motion data according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of another motion data processing method according to an embodiment of the present application;

fig. 4 is a schematic view of an amusement park mode in which an intelligent bracelet is manually opened according to an embodiment of the present application;

fig. 5 is a schematic diagram of determining a motion trajectory curve according to motion data according to an embodiment of the present application;

fig. 6 is a schematic diagram of a motion trajectory curve corresponding to a roller coaster project according to an embodiment of the present application;

fig. 7 is a schematic diagram of a motion stimulation degree curve corresponding to a roller coaster project according to an embodiment of the present application;

fig. 8 is a schematic diagram of a curve for determining a degree of motor stimulation according to an embodiment of the present application;

fig. 9 is a schematic view of a motion curve corresponding to a roller coaster project according to an embodiment of the present disclosure;

fig. 10 is a schematic diagram illustrating a motion curve corresponding to a roller coaster event according to an embodiment of the present disclosure;

FIG. 11 is a schematic diagram of a motion profile including key information according to an embodiment of the present disclosure;

FIG. 12 is a schematic view of a motion profile provided by an embodiment of the present application;

fig. 13 is a schematic structural diagram of a motion data processing apparatus according to an embodiment of the present application;

fig. 14 is a schematic structural diagram of a communication device according to an embodiment of the present application.

Detailed Description

The method for processing the motion data provided by the embodiment of the present application may be applied to a terminal with a display screen, or other terminals with a screen display function that may appear in the future, and the embodiment of the present application does not limit this.

In the embodiments of the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. In the description of the text of the present application, the character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

The method for processing motion data provided by the embodiment of the application can be applied to amusement park projects, such as amusement projects with high altitude or stimulation attributes, such as roller coasters, large pendulums, ferris wheels, and the like, and can also be applied to other sports projects with high altitude or stimulation attributes, such as scenes of gliding, bungee jumping, drifting, parachuting, and the like. For example, please refer to fig. 1, where fig. 1 is a schematic diagram of a possible application scenario provided in the embodiment of the present application, where the application scenario may include a wearable device and a terminal, after a user starts an amusement park mode of the wearable device, the wearable device starts to continuously detect a user state, and if it is detected that the user enters a play state, the wearable device starts to collect relevant motion data of the user; and if the user quits the playing state, stopping collecting the motion data, sending the relevant motion data collected in the motion process of the user to the terminal, generating a motion trail curve of the user by the terminal according to the motion data, and displaying the motion trail curve of the user to the user by combining a map engine, wherein the displayed content is single, so that the user experience is poor.

In order to solve the problem that the display content is single and the user experience is not good in the prior art, through a long-term experiment, an embodiment of the present application provides a method for processing athletic data, as shown in fig. 2, fig. 2 is a schematic flow diagram of the method for processing athletic data provided by the embodiment of the present application, and the method for processing athletic data may include: s201, acquiring motion data of a user; s202, determining a motion curve of the user according to the motion data; the motion curve is used for indicating a motion track of the user in the motion process and the motion stimulation degree in the motion process; s203 displays the motion curve. Compared with the prior art in which only the movement track is displayed to the user, the movement data processing method provided by the embodiment of the application determines the movement curve for indicating the movement track of the user in the movement process and the movement stimulation degree of the user in the movement process, so that the movement track of the user in the movement process is displayed to the user, the movement stimulation degree of the user in the movement process is also displayed to the user during visual display, the display content is enriched, and the user experience is improved.

As can be seen from the above description, in the embodiment of the present application, when displaying the movement track of the user during movement and the movement stimulation degree during movement, the two different types of information are displayed by using one movement curve, and when the method for processing movement data provided in the embodiment of the present application is described in detail later, the two different types of information are illustrated by using one movement curve as an example. Of course, the two curves may also be used for displaying, that is, one motion curve represents a motion trajectory of the user during the motion process, and one motion curve represents a motion stimulation degree during the motion process, here, the embodiment of the present application is only described by taking an example that one motion curve represents the motion trajectory of the user during the motion process and the motion stimulation degree during the motion process, but the embodiment of the present application is not limited thereto.

It should be noted that, when the technical solution provided by the embodiment of the present application is implemented, in a possible scenario, an execution subject of the motion data processing method provided by the embodiment of the present application is a terminal, for example, a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Mobile Internet Device (MID), and the like, and when the execution subject is the terminal, as shown in fig. 1, wearable devices, for example, a smart watch, a smart bracelet, and the like, are mainly responsible for acquiring motion data in a motion process. In another possible scenario, if data processing capability and screen limitation of the wearable device are not considered, an execution subject of the motion data processing method provided in the embodiment of the present application may also be the wearable device, that is, the wearable device is not only responsible for collecting motion data in a motion process, but also executes the motion data processing method provided in the embodiment of the present application based on the collected motion data. In general, in the motion data processing process, it is considered that the wearable device only needs to be responsible for collecting motion data in the motion process and sending the collected motion data to the terminal because the data processing capability and the screen of the wearable device are limited, the terminal executes the motion data processing method provided by the embodiment of the present application based on the received motion data, that is, a first possible scenario, and then, the motion data processing method provided by the embodiment of the present application will be described in detail by taking the first possible scenario as an example.

Hereinafter, the technical solution of the motion data processing method provided in the present application will be described in detail through a detailed embodiment. It is to be understood that the following detailed description may be combined with other embodiments, and that the same or similar concepts or processes may not be repeated in some embodiments.

Fig. 3 is a schematic flow chart of another motion data processing method according to an embodiment of the present application, and for example, please refer to fig. 3, the motion data processing method may include:

s301, acquiring motion data of the user.

The motion data is the motion data of the user in the whole motion process, and the motion data not only comprises data used for calculating the motion trail of the user, but also comprises the motion data used for calculating the motion stimulation degree. For example, the data for calculating the movement trajectory of the user may be acquired by a GPS, or may be acquired by a nine-axis sensor (a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer). For example, in the embodiment of the present application, the factor affecting the degree of the motion stimulation may include at least one of a motion speed, a motion acceleration, a motion altitude, or a heart rate, and when the factor affecting the degree of the motion stimulation includes the motion speed, the motion acceleration, the motion altitude, and the heart rate, the motion data used for calculating the degree of the motion stimulation may include GPS data, barometer data, and heart rate data, wherein the coordinate data is used for calculating the motion speed and the motion acceleration of the user; the barometer data is used for calculating the movement height of the user; the heart rate data is used to calculate the heart rate of the user while exercising.

It is understood that the more factors considered in calculating the degree of motor stimulation, the more accurate the value corresponding to the calculated degree of motor stimulation. It should be noted that, in the embodiments of the present application, the factors that influence the degree of the exercise stimulation may include at least one of exercise speed, exercise acceleration, exercise height, or heart rate, but the embodiments of the present application are not limited thereto. When describing the exercise stimulation degree, in order to improve the accuracy of the exercise stimulation degree, the four factors that affect the exercise stimulation degree, including the exercise velocity, the exercise acceleration, the exercise height, and the heart rate, will be described as examples.

When the terminal acquires the motion data of the user, the motion data sent by the wearable device can be received through Bluetooth or a wireless network. Taking wearable equipment as an example of an intelligent bracelet, after a user opens an amusement park mode of the intelligent bracelet, the intelligent bracelet continuously collects motion data of the user and determines whether the user enters a playing state or not according to the motion data of the user. For example, if the smart band determines that the movement speed of the user is greater than a first speed threshold value according to the movement data acquired at the first time, and/or the movement height is greater than the first height threshold value, it indicates that the user enters the play state at the first time, and continues to acquire the movement data of the user after determining that the user enters the play state, and if the smart band determines that the movement speed is less than a second speed threshold value according to the movement data acquired at the second time, and the movement height is less than a second height threshold value, it indicates that the user exits the movement state at the second time, and stops acquiring the movement data of the user when determining that the user exits the play state, thereby acquiring the movement data of the user in the whole movement process from the first time to the second time; and after acquiring the motion data in the whole motion process, sending the motion data in the whole process to the terminal through Bluetooth or a wireless network so that the terminal can acquire the motion data in the whole motion process of the user. For example, values of the first speed threshold, the first altitude threshold, the second speed threshold, and the second altitude threshold may be set according to actual needs, where the embodiments of the present application are not specifically limited to the values of the first speed threshold, the first altitude threshold, the second speed threshold, and the second altitude threshold.

It can be understood that, before the motion data in the whole process is sent to the terminal through bluetooth or a wireless network, the smart band determines the duration of the whole process according to the difference between the second time and the first time, and if the duration is less than the time threshold, it indicates that the whole process is not a complete item playing process, so the motion data collected in the motion process is invalid data, the motion data collected in the whole process can be discarded, and the motion data of the user is collected again when the user enters a playing state next time. If the duration is greater than or equal to the time threshold, the whole process is a complete project playing process, so that the motion data acquired in the motion process is valid data, and the motion data acquired in the whole process is sent to the terminal through the Bluetooth or the wireless network, so that the terminal acquires the motion data of the user in the whole motion process and processes the motion data. The intelligent bracelet can effectively screen out invalid movement data by comparing the duration with the time threshold, and avoids sending the invalid movement data to the terminal, so that the terminal does not need to process the invalid movement data, and the power consumption of the terminal is reduced.

In the above description, the smart band determines whether the user enters the playing state at the first moment according to the movement speed and/or the movement height at the first moment, and the movement speed and/or the movement height at the first moment need to be calculated first; similarly, before determining whether the user quits the playing state at the second time according to the movement speed and the movement altitude at the second time, the movement speed and the movement altitude at the second time need to be calculated, and the calculation method of the movement speed at the first time and the calculation method of the movement altitude at the first time are similar to the calculation method of the movement speed at the second time and the calculation method of the movement altitude at the second time, respectively, taking calculating the movement speed at the first time t0 and the movement altitude at the first time as an example, when calculating the movement speed at the first time, the movement speed V at the first time t0 may be obtained by using a GPS_t0See equation 1 below.

Wherein, S (GPS)_t0,GPS_t0-1) Representing the physical distance of the first time t0 from its previous time t0-1, and at represents the time difference between the first time t0 and its previous time t 0-1.

In calculating the height of movement at the first time t0, the height of movement Δ H at the first time t0 may be calculated using barometer data_t0See equation 2 below.

ΔH_t0＝44300*((Pa_ref/Pa₀)^1/5.256-(Pa_t0/Pa₀)^1/5.256) Equation 2

Wherein Pa is_t0Indicates the atmospheric pressure, Pa, at a first time t0_refAtmospheric pressure, Pa, representing an initial state₀Indicating the local standard atmospheric pressure.

After the moving speed at the first time t0 and the moving altitude at the first time t0 are calculated by the above equation 1 and equation 2, respectively, it can be determined whether the user enters the playing state at the first time according to the moving speed at the first time t0, and/or the moving altitude, and if the moving speed of the user is greater than the first speed threshold, and/or the moving altitude is greater than the first altitude threshold, it indicates that the user enters the playing state at the first time, the first time t0 can be understood as the starting time of the sports item, and the second time t1 can be understood as the ending starting time of the sports item.

It should be noted that, since the method for calculating the movement speed at the second time t1 and the method for calculating the movement altitude at the second time t1 are similar to the method for calculating the movement speed at the first time and the method for calculating the movement altitude at the first time, respectively, reference may be made to the related description of the method for calculating the movement speed at the first time and the method for calculating the movement altitude at the first time, and here, the method for calculating the movement speed at the second time and the method for calculating the movement altitude at the second time are similar to the method for calculating the movement speed at the first time, respectively, and details of the embodiments of the present application are not repeated.

In the above description, when the amusement park mode of the smart band is turned on, at least three possible implementations may be included, for example. In a possible implementation manner, the smart band may automatically turn on the amusement park mode of the smart band when determining that the user currently enters the amusement park area through the positioning system in combination with the map, and although the intelligent control is implemented in this manner, there may be a case where the amusement park mode is turned on by mistake, for example, the user may simply pass through the amusement park area, and does not go to play in the amusement park. In another possible implementation manner, a user may manually open an amusement park mode of an intelligent bracelet, for example, please refer to fig. 4, where fig. 4 is a schematic diagram of the amusement park mode of the manual intelligent bracelet provided in this embodiment of the present application, because the size of a screen of the intelligent bracelet is limited, the user may first manually click through the screen of the terminal to enter a control program of the intelligent bracelet to select the amusement park mode, and then click an open button in the amusement park mode to control the intelligent bracelet to open the amusement park mode. In another possible implementation manner, the amusement park mode can be selected by entering a control program of the intelligent bracelet through voice control, and then the amusement park mode is opened by controlling the intelligent bracelet through voice. Of course, the method and the device can also be operated directly on the screen of the smart band to control the smart band to open the amusement park mode, and specifically, the setting can be performed according to actual needs.

After the exercise data of the user is acquired through S301, the exercise trajectory curve and the exercise stimulation degree curve may be determined according to the exercise data, that is, the following S302 is performed:

and S302, respectively determining a motion track curve and a motion stimulation degree curve according to the motion data.

The motion trail curve is used for representing a motion trail of a user in a motion process, and the motion stimulation degree curve is used for representing a corresponding relation between time and motion stimulation degree in the motion process.

When determining a motion trajectory curve according to motion data, for example, as shown in fig. 5, fig. 5 is a schematic diagram of determining a motion trajectory curve according to motion data provided in an embodiment of the present application, and a motion item corresponding to the motion data may be determined based on a big data analysis technique; for example, the motion item may be any one of an amusement park item such as a large pendulum, a free fall, or a roller coaster, if the motion trajectory corresponding to the motion item belongs to a first type, the motion trajectory of the first type may be understood as a motion trajectory with lower complexity, and since each motion item belonging to the first type is preset with a respective motion trajectory curve, the preset motion trajectory curve corresponding to the motion item is read, and the preset motion trajectory curve corresponding to the motion item is determined as the motion trajectory curve corresponding to the motion item. For example, if the motion trajectory corresponding to the motion item belongs to a second type, and the motion trajectory of the second type can be understood as a motion trajectory with higher complexity, a curve fitting technology is adopted to fit the motion data to obtain a motion trajectory curve. Wherein the complexity of the motion trajectory of the motion item belonging to the second type is higher than the complexity of the motion trajectory of the motion item belonging to the first type. For example, the motion trail corresponding to the motion item such as the large pendulum or the free fall belongs to a first type, and the motion trail corresponding to the motion item such as the roller coaster belongs to a second type.

It can be understood that, in the embodiment of the present application, for a motion item having a simple motion trajectory with a low complexity, when determining a corresponding motion trajectory curve thereof, a motion trajectory curve corresponding to the motion item may be obtained without calculating according to currently obtained motion data, but a preset motion trajectory curve corresponding to the motion item is read, and the preset motion trajectory curve corresponding to the motion item is determined as the motion trajectory curve corresponding to the motion item, so that not only the complexity of terminal calculation may be reduced, but also the motion trajectory may be optimized, and the accuracy of the motion trajectory curve is improved. For example, in the implementation example of the present application, taking a motion item as a roller coaster, a motion trajectory curve corresponding to the roller coaster item may be as shown in fig. 6, and fig. 6 is a schematic diagram of a motion trajectory curve corresponding to the roller coaster item provided in the embodiment of the present application.

When determining the exercise stimulation degree curve according to the exercise data, for example, the exercise stimulation degree at each of the at least two time points in the exercise process may be determined according to the exercise data at the second time point; and fitting the exercise stimulation degrees of at least two time points by adopting a polynomial fitting technology to obtain an exercise stimulation degree curve. It can be understood that, when the exercise stimulation degree curve is obtained by fitting the exercise stimulation degrees at the second time points, the accuracy of the exercise stimulation degree curve obtained by fitting is higher as the number of the second time points used is larger. For example, in this embodiment of the application, the exercise stimulation degree of each second time point in the exercise process may be determined according to the exercise data of each second time point in the exercise process, and then a polynomial fitting technology is used to fit the exercise stimulation degree of each second time point in the exercise process, so as to obtain an exercise stimulation degree curve with higher accuracy. Similarly, taking a sports item as a roller coaster as an example, the sports stimulation degree curve corresponding to the roller coaster item can be seen in fig. 7, and fig. 7 is a schematic diagram of a sports stimulation degree curve corresponding to a roller coaster item provided in the embodiment of the present application.

It can be seen that the motor stimulation degree of each second time point needs to be calculated when the motor stimulation degree curve is obtained through fitting. As can be seen from the foregoing description, in the embodiment of the present application, the factors affecting the exercise stimulation degree include four factors, i.e., exercise speed, exercise acceleration, exercise height, and heart rate, so that when the stimulation degree at the second time point is calculated according to the four factors, i.e., the exercise speed, the exercise acceleration, the exercise height, and the heart rate at the second time point, normalization processing may be performed on the exercise speed, the exercise acceleration, the exercise height, and the heart rate corresponding to the second time point, respectively, to obtain the exercise speed, the exercise acceleration, the exercise height, and the heart rate after normalization processing; and then determining the exercise stimulation degree of the second time point according to the exercise speed, the exercise acceleration, the exercise height and the heart rate after the normalization processing. Thus, after determining the exercise stimulation degree at the second time point, a polynomial fitting technique may be used to fit the exercise stimulation degree at each second time point in the exercise process, so as to obtain an exercise stimulation degree curve representing a corresponding relationship between time and exercise stimulation degree in the exercise process, as shown in fig. 8, where fig. 8 is a schematic diagram of determining the exercise stimulation degree curve provided in this embodiment of the present application.

When the exercise velocity, the exercise acceleration, the exercise height, and the heart rate corresponding to any one of the second time points t2 are normalized, the exercise velocity normalization processing method, the exercise acceleration normalization processing method, the exercise height normalization processing method, and the heart rate normalization processing method are similar, and taking the exercise velocity normalization processing method as an example, the exercise velocity V is normalized_t2The normalization process is performed as shown in equation 3 below.

Wherein, V_t2' denotes the normalized movement velocity, V, at the second time t2_t2Represents the movement speed, V, of the second point in time t2_maxAnd V_minThe theoretical maximum and minimum values of the movement speed are expressed, and can be obtained by a big data analysis technology.

It should be noted that, since the method for normalizing the motion acceleration, the method for normalizing the motion altitude, and the method for normalizing the heart rate at the second time point t2 are all similar to the method for normalizing the motion velocity at the second time point t2, reference may be made to the related description of the method for normalizing the motion velocity at the second time point t2, and here, the method for normalizing the motion acceleration, the method for normalizing the motion altitude, and the method for normalizing the heart rate at the second time point t2 is not repeated in this embodiment.

The movement speeds V after the normalization processing of the second time points are respectively obtained through calculation_t2', the motion acceleration,a_t2' height of motion Δ H_t2' and heart rate Hr_t2' thereafter, the processed movement velocity V can be normalized according to the second time point_t2', acceleration of motion, a_t2' height of motion Δ H_t2' and heart rate Hr_t2', the exercise stimulation degree F at the second time point can be calculated according to the following formula 4_t2。

F_t2＝α*V′_t2+β*a′_t2+λ*ΔH′_t2+μ*Hr′_t2Equation 4

Wherein, F_t2Representing the degree of motor stimulation F at a second point in time_t2And alpha, beta, lambda and mu represent the motion speed, the motion acceleration, the motion height and the heart rate weight coefficient, and are obtained by a big data analysis technology.

After the exercise stimulation degree of each second time point t2 in the exercise process is calculated through the above equation 3 and equation 4, a polynomial fitting technique may be used to fit the exercise stimulation degree of each second time point in the exercise process, so as to obtain an exercise stimulation degree curve f (t) representing the corresponding relationship between time and exercise stimulation degree in the exercise process, which may be referred to as equation 5 below.

Where M represents the polynomial order, ω₀,...,ω_MCoefficient of the polynomial expression, t represents time, F (t) represents stimulation degree F corresponding to t time point_t。

In this way, after obtaining a motion trajectory curve representing a motion trajectory during a motion of the user and a motion stimulation degree curve representing a correspondence between time and a motion stimulation degree during the motion from the motion data, respectively, if the two curves are displayed during displaying, the motion trajectory curve and the motion stimulation degree curve can be directly displayed on the screen after S302 is executed. In order to make the display effect of the screen better and concise when displaying the motion trail and the motion stimulation degree to the user through the screen, the calculated motion trail curve may be superimposed with the motion stimulation degree curve to obtain a motion curve for indicating the motion trail and the motion stimulation degree of the user during the motion process, that is, the following S303 is executed:

and S303, determining a motion curve according to the motion track curve and the motion stimulation degree curve.

For example, when the motion trajectory curve and the motion stimulation degree curve are superimposed to obtain the motion curve, at least two possible implementation manners may be included, and in one possible implementation manner, colors corresponding to the motion stimulation degrees of at least two first time points in the motion stimulation degree curve may be determined according to a mapping relationship between the motion stimulation degrees and the colors; and modifying the color corresponding to each of the at least two first time points in the motion trail curve into the color corresponding to the motion stimulation degree of the first time point to obtain the motion curve.

It can be understood that, when the color corresponding to the first time point in the motion trajectory curve is modified into the color corresponding to the motion stimulation degree of the first time point, the greater the number of the modified first time points is, the higher the accuracy of the motion curve obtained by corresponding superposition is. In the embodiment of the present application, in order to improve the accuracy of the motion curve, the color corresponding to each first time point in the motion trajectory curve may be modified to the color corresponding to the motion stimulation degree of the first time point. For example, when the degree of the motion stimulation is represented by a color, the darker the color, the more intense the degree of the motion stimulation.

In this possible implementation manner, when the color corresponding to the first time point in the motion trajectory curve is modified to the color corresponding to the motion stimulation degree at the first time point according to the mapping relationship between the motion stimulation degree and the color, the mapping relationship between the motion stimulation degree and the color may include, but is not limited to, a mapping relationship between the motion stimulation degree and the color temperature, and a mapping relationship between the motion stimulation degree and the spectrum, and of course, other motion stimulation degrees and a mapping relationship between real numbers and colors may also be included. The mapping relationship between the degree of the motor stimulation and the color temperature can be seen in the following formula 6.

K_t＝99000*F_t+1000 equation 6

Wherein, F_tRepresenting the stimulation degree corresponding to the t time point; k_tThe color temperature value at time t is expressed in kelvin.

Through the above formula 6, the color temperature value corresponding to the exercise stimulation degree corresponding to each time point in the exercise stimulation degree curve can be determined, and then the color corresponding to each first time point in the exercise trajectory curve is modified to the color temperature value corresponding to the exercise stimulation degree of the first time point. Taking the first three time points in the motion stimulation degree curve as an example, determining, by using the formula 6, that a color temperature value corresponding to the motion stimulation degree corresponding to a first time point in the motion stimulation degree curve is a first color temperature value, a color temperature value corresponding to the motion stimulation degree corresponding to a second time point in the motion stimulation degree curve is a second color temperature value, and a color temperature value corresponding to the motion stimulation degree corresponding to a third time point in the motion stimulation degree curve is a third color temperature value, modifying the color corresponding to the first time point in the motion trajectory curve into the first color temperature value, modifying the color corresponding to the second time point in the motion trajectory curve into the second color temperature value, and modifying the color corresponding to the third time point in the motion trajectory curve into the third color temperature value; it can be understood that the modification of the colors corresponding to other time points in the motion trail curve is similar to the modification method of the first three time points in the motion trail curve, so that after the color corresponding to each first time point in the motion trail curve is modified into the color temperature value corresponding to the motion stimulation degree of the first time point, a motion trail after superposition can be obtained, and the motion trail is not only used for indicating the motion trail of the user in the motion process, but also can indicate the motion stimulation degree in the motion process.

In another possible implementation manner, line types corresponding to the exercise stimulation degrees of at least two first time points in the exercise stimulation degree curve may be determined according to a mapping relationship between the exercise stimulation degrees and the line types; and modifying the line type corresponding to each first time point in the at least two first time points in the motion trail curve into the line type corresponding to the motion stimulation degree of the first time point to obtain the motion curve. For example, the line type may be divided by the thickness of the line, and for example, when the exercise stimulation degree is represented by the thickness of the line, the deeper the line, the more intense the exercise stimulation degree. Of course, the division may be performed by a solid line or a broken line. For example, as shown in fig. 9, fig. 9 is a schematic view of a motion curve corresponding to a roller coaster project provided in an embodiment of the present application.

It can be understood that, when the line type corresponding to the first time point in the motion trajectory curve is modified into the line type corresponding to the motion stimulation degree of the first time point, the more the number of the modified first time points is, the higher the accuracy of the motion curve obtained by corresponding superposition is. In this embodiment of the application, in order to improve the accuracy of the motion curve, the line type corresponding to each first time point in the motion trajectory curve may be modified to the line type corresponding to the motion stimulation degree of the first time point.

It is understood that, in the embodiment of the present application, when a motion curve and a motion stimulation degree curve are superimposed to obtain a motion curve indicating a motion curve during a user's motion and a motion stimulation degree during the motion, the above two possible implementations are only used as examples, but the embodiment of the present application is not limited thereto. After obtaining the motion curve indicating the motion trajectory during the user's motion and the degree of the motor stimulus during the motion, the motion curve may be stored and displayed. By storing the motion curve, the user can conveniently check the motion curve subsequently, so that the user can be helped to recall the wonderful moment in the whole motion process, and the user experience is improved.

And S304, displaying the motion curve.

After determining the motion curve according to the motion trajectory curve and the motion stimulation degree curve, prompt information may be output to the user, for example, whether to view the motion curve, and the motion curve may be displayed to the user when receiving a viewing instruction from the user; of course, after determining the motion curve according to the motion trajectory curve and the motion stimulation degree curve, the motion curve may be directly displayed to the user without outputting a prompt message to the user, and in general, since the display screen of the terminal is larger than the display screen of the smart band, in order to improve the viewing experience of the user, the motion curve may be directly displayed on the display screen of the terminal, for example, please refer to fig. 10, where fig. 10 is a schematic diagram for displaying the motion curve corresponding to the coaster project provided in the embodiment of the present application. Certainly, if the situation that the display screen of the bracelet is small is not considered, the terminal can also send the motion curve to the intelligent bracelet through bluetooth or a wireless network after generating the motion curve, and display the motion curve on the display screen of the intelligent bracelet.

The embodiment shown in fig. 3 above describes in detail that, in order to enrich the display content to improve the user experience, when performing visual display on the user, the motion trail of the user in the motion process can be displayed to the user, and the motion stimulation degree of the user in the motion process can be synchronously displayed to the user, so that the display content is enriched, and the user experience is improved. In the embodiment of the application, besides the synchronous display of the exercise stimulation degree during exercise to the user, the key information during exercise can be further displayed to the user, wherein the key information comprises at least one of the maximum exercise height, the maximum exercise speed, the maximum heart rate or the exercise stimulation degree score. It can be understood that, when the key information is displayed, the more the displayed key information is, the richer the corresponding display content is, and the better the user experience is. In order to make the display content richer, in the embodiment of the application, the maximum exercise height, the maximum exercise speed, the maximum heart rate and the exercise stimulation degree score can also be displayed to the user.

For example, when the maximum movement height in the whole movement process is determined, since the movement height corresponding to each time in the whole movement process can be calculated, the maximum movement height in the whole movement process can be determined through comparison; similarly, when determining the maximum exercise height and the heart rate in the whole exercise process, the exercise speed and the heart rate corresponding to each time in the whole exercise process can be calculated, and therefore, the maximum exercise speed and the maximum heart rate in the whole exercise process can be determined through comparison.

For example, when determining the exercise stimulation degree score, the exercise stimulation degree curve may be integrated based on the exercise stimulation degree curve obtained in S302 to obtain an integration result; then, the ratio of the integration result to the duration of the exercise process is calculated, so that the exercise stimulation degree score S of the whole exercise process can be calculated according to the ratio of the integration result to the duration of the exercise process, which can be referred to the following formula 7_thrill. Wherein the duration of the movement process is determined according to the ending time and the starting time of the movement process.

Wherein S is_thrillScore of motor stimulation degree, t, representing the movement item₀Represents the starting time of the sports item; t is t₁Indicating the end time of the sporting event.

It can be understood that, when determining the exercise stimulation degree score of the whole exercise process according to the ratio of the integration result to the duration time of the exercise process, the calculated ratio can be directly determined as the exercise stimulation degree score of the whole exercise process, that is, the ratio calculated by the above formula 7 is determined as the exercise stimulation degree score of the whole exercise process; of course, in order to make the score value of the exercise stimulation degree fall within a certain score interval, the ratio of the integration result to the duration time of the exercise process may be normalized to obtain a normalized ratio; and processing the normalized ratio based on a preset scoring interval to obtain the exercise stimulation degree score. Taking the score interval as [0,100] as an example, the normalized ratio can be processed by the following formula 8 to obtain the exercise stimulation degree score S.

S＝round(100*S′_thrill) Equation 8

Wherein S represents a motor stimulation degree score, S'_thrillExpressing the normalized ratio; round () represents a rounding function.

After the maximum exercise height, the maximum exercise speed, the maximum heart rate and the exercise stimulation degree in the exercise process are respectively determined, the exercise track and the exercise stimulation degree in the exercise process of the user can be displayed to the user, and meanwhile, the user can be given such key information together.

It can be seen that, with the above-mentioned embodiment shown in fig. 3, when playing a sports item, for example, a roller coaster item, the terminal may generate a motion curve corresponding to the roller coaster item according to the motion data during the motion, and display the motion curve to the user. If the wearable device detects that the user enters the playing state again, the wearable device may also collect the motion data in the motion process, and send the motion data to the terminal, so that the terminal determines the motion curve corresponding to the motion item according to the motion data, taking the motion item as a large pendulum item as an example, an obtaining method of the motion curve corresponding to the large pendulum item is similar to that of the motion curve corresponding to the roller coaster item in the embodiment shown in fig. 3, and reference may be made to the above-mentioned related description of the obtaining method of the motion curve corresponding to the roller coaster item, and details of the embodiment of the present application are not repeated herein. Therefore, by the method for processing the motion data provided by the embodiment of the application, different motion items can be continuously identified through the motion data, the motion data in each motion process is obtained, the motion curve corresponding to each motion item is generated based on the motion data in each motion process, and the motion curve corresponding to each motion item is stored, so that when a user subsequently views the motion curve, the user can be helped to recall the wonderful moment in the whole motion process through the motion curve, for example, please refer to fig. 12, which is a schematic diagram of viewing the motion curve provided by the embodiment of the application, and it can be seen that when the user subsequently views the motion curve, the user can firstly enter an amusement park mode, and after entering the amusement park mode, the terminal can display the historical playing items played before to the user, when it is determined that a user clicks a certain motion item, for example, the user clicks a roller coaster item, a motion curve corresponding to the roller coaster item is displayed to the user, so as to help the user to recall a wonderful moment in the whole motion process through the motion curve.

Fig. 13 is a schematic structural diagram of a motion data processing device 130 according to an embodiment of the present application, and for example, please refer to fig. 13, the motion data processing device 130 may include:

an obtaining unit 1301 is configured to obtain motion data of a user.

A processing unit 1302, configured to determine a motion curve of the user according to the motion data; the motion curve is used for indicating the motion trail of the user in the motion process and the motion stimulation degree in the motion process.

And a display unit 1303 for displaying the motion curve.

Optionally, the processing unit 1302 is configured to determine a motion trajectory curve and a motion stimulation degree curve according to the motion data; determining a motion curve according to the motion trail curve and the motion stimulation degree curve; the motion trail curve is used for representing a motion trail of a user in a motion process, and the motion stimulation degree curve is used for representing a corresponding relation between time and motion stimulation degree in the motion process.

Optionally, the processing unit 1302 is configured to determine, according to a mapping relationship between the exercise stimulation degrees and the colors, colors corresponding to the exercise stimulation degrees of at least two first time points in the exercise stimulation degree curve; and modifying the color corresponding to each of the at least two first time points in the motion trail curve into the color corresponding to the motion stimulation degree of the first time point to obtain the motion curve.

Optionally, the processing unit 1302 is specifically configured to determine an exercise item corresponding to the exercise data based on a big data analysis technology; and determining a motion trail curve according to the motion items.

Optionally, the processing unit 1302 is specifically configured to determine, if the motion trajectory corresponding to the motion item belongs to the first type, a preset motion trajectory curve corresponding to the motion item as a motion trajectory curve; the motion items belonging to the first type correspond to respective motion trail curves; if the motion trail corresponding to the motion item belongs to the second type, fitting the motion data by adopting a curve fitting technology to obtain a motion trail curve; wherein the complexity of the motion trajectory of the motion item belonging to the second type is higher than the complexity of the motion trajectory of the motion item belonging to the first type.

Optionally, the motion data includes at least one of a motion speed, a motion acceleration, a motion altitude, or a heart rate.

The processing unit 1302 is specifically configured to determine, according to the motion data of each second time point of the at least two time points in the motion process, a motion stimulation degree of the second time point; and fitting the exercise stimulation degrees of at least two time points by adopting a polynomial fitting technology to obtain an exercise stimulation degree curve.

Optionally, the motion data includes motion speed, motion acceleration, motion altitude, and heart rate.

The processing unit 1302 is specifically configured to perform normalization processing on the motion speed, the motion acceleration, the motion height, and the heart rate corresponding to the second time point, respectively, so as to obtain a processed motion speed, motion acceleration, motion height, and heart rate; and determining the exercise stimulation degree of the second time point according to the processed exercise speed, the exercise acceleration, the exercise height and the heart rate.

Optionally, the display unit 1303 is further configured to display key information in the motion process; wherein the key information comprises at least one of a maximum movement height, a maximum movement speed, a maximum heart rate, or a movement stimulation level score.

Optionally, the processing unit 1302 is further configured to integrate the exercise stimulation degree curve to obtain an integration result; determining a motor stimulation degree score according to the ratio of the integral result to the duration time of the motor process; wherein the duration of the movement process is determined according to the ending time and the starting time of the movement process.

Optionally, the processing unit 1302 is specifically configured to normalize the ratio to obtain a normalized ratio; and processing the normalized ratio based on a preset scoring interval to obtain a motor stimulation degree score.

Optionally, the exercise data includes an exercise speed and an exercise height, and the processing unit 1302 is further configured to determine that the user enters the exercise state if the exercise speed is greater than a first speed threshold and/or the exercise height is greater than a first height threshold; and if the movement speed is less than the second speed threshold value and the movement height is less than the second height threshold value, determining that the user exits the movement state.

The motion data processing apparatus 130 shown in this embodiment of the application can execute the technical solution of the motion data processing method shown in any one of the above embodiments, and the implementation principle and the beneficial effect thereof are similar to those of the motion data processing method, and are not described herein again.

Fig. 14 is a schematic structural diagram of a communication device 140 according to an embodiment of the present application, and for example, please refer to fig. 14, where the communication device 140 includes a processor 1401 and a memory 1402, where the memory stores a computer program, and the processor 1401 executes the computer program stored in the memory 1402, so as to enable the device to execute the technical solution of the method for processing athletic data according to any one of the embodiments, and an implementation principle and an advantageous effect of the method are similar to those of the method for processing athletic data, and are not described again here.

An embodiment of the present application further provides a communication apparatus, which may include: a processor and interface circuitry.

The processor is configured to run the code instruction to execute the technical solution of the motion data processing method shown in any one of the embodiments, and the implementation principle and the beneficial effect of the processor are similar to those of the motion data processing method, and are not described herein again.

The embodiment of the present application further provides a readable storage medium, which is used for storing instructions, and when the instructions are executed, the method for processing motion data as described in any of the above embodiments is implemented, and the implementation principle and the beneficial effect of the method for processing motion data are similar to those of the method for processing motion data, and are not described herein again.

The processor in the above embodiments may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in a Random Access Memory (RAM), a flash memory, a read-only memory (ROM), a programmable ROM, an electrically erasable programmable memory, a register, or other storage media that are well known in the art. The storage medium is located in a memory, and a processor reads instructions in the memory and combines hardware thereof to complete the steps of the method.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Claims

1. A method for processing motion data, comprising:

acquiring motion data of a user;

determining a motion curve of the user according to the motion data; the motion curve is used for indicating a motion track of the user in the motion process and a motion stimulation degree in the motion process;

and displaying the motion curve.

2. The method of claim 1, wherein determining the motion profile of the user from the motion data comprises:

respectively determining a motion track curve and a motion stimulation degree curve according to the motion data; the motion trail curve is used for representing a motion trail of the user in a motion process, and the motion stimulation degree curve is used for representing a corresponding relation between time and motion stimulation degree in the motion process;

and determining the motion curve according to the motion track curve and the motion stimulation degree curve.

3. The method of claim 2, wherein said determining the motion profile from the motion trajectory profile and the motion stimulation level profile comprises:

determining colors corresponding to the exercise stimulation degrees of at least two first time points in the exercise stimulation degree curve according to the mapping relation between the exercise stimulation degrees and the colors;

and modifying the color corresponding to each first time point in the at least two first time points in the motion trail curve into the color corresponding to the motion stimulation degree of the first time point to obtain the motion curve.

4. A method according to claim 2 or 3, wherein determining a motion trajectory profile from the motion data comprises:

determining a motion item corresponding to the motion data based on a big data analysis technology;

and determining the motion trail curve according to the motion item.

5. The method of claim 4, wherein determining the motion profile from the motion item comprises:

if the motion trail corresponding to the motion item belongs to a first type, determining a preset motion trail curve corresponding to the motion item as the motion trail curve; the motion items belonging to the first type correspond to respective motion trail curves;

if the motion trail corresponding to the motion item belongs to a second type, fitting the motion data by adopting a curve fitting technology to obtain a motion trail curve; wherein the complexity of the motion trajectory of the motion item belonging to the second type is higher than the complexity of the motion trajectory of the motion item belonging to the first type.

6. The method of any of claims 2-5, wherein the motion data includes at least one of a motion speed, a motion acceleration, a motion altitude, or a heart rate, and wherein determining a motion stimulation level curve from the motion data includes:

determining the exercise stimulation degree of each second time point in at least two time points in the exercise process according to the exercise data of the second time point;

and fitting the exercise stimulation degrees of the at least two time points by adopting a polynomial fitting technology to obtain the exercise stimulation degree curve.

7. The method of claim 6, wherein the exercise data comprises exercise speed, exercise acceleration, exercise height and heart rate, and the determining the exercise stimulation level at each of at least two time points during the exercise according to the exercise data at the second time point comprises:

respectively carrying out normalization processing on the movement speed, the movement acceleration, the movement height and the heart rate corresponding to the second time point to obtain the processed movement speed, movement acceleration, movement height and heart rate;

and determining the exercise stimulation degree of the second time point according to the processed exercise speed, exercise acceleration, exercise height and heart rate.

8. The method according to any one of claims 2-7, further comprising:

displaying key information in the motion process; wherein the key information comprises at least one of a maximum movement height, a maximum movement speed, a maximum heart rate, or a motor stimulation level score.

9. The method of claim 8, further comprising:

integrating the exercise stimulation degree curve to obtain an integration result;

determining the exercise stimulation degree score according to the ratio of the integration result to the duration time of the exercise process; wherein the duration of the motion process is determined according to an end time and a start time of the motion process.

10. The method of claim 9, wherein determining the motor stimulation level score based on a ratio of the integration result and a duration of the motor process comprises:

normalizing the ratio to obtain a normalized ratio;

and processing the normalized ratio based on a preset scoring interval to obtain the exercise stimulation degree score.

11. The method of any of claims 1-10, wherein the motion data comprises a motion speed and a motion altitude, the method further comprising:

if the movement speed is greater than a first speed threshold value and/or the movement height is greater than a first height threshold value, determining that the user enters a movement state;

and if the movement speed is smaller than a second speed threshold value and the movement height is smaller than a second height threshold value, determining that the user exits the movement state.

12. An apparatus for processing motion data, comprising:

an acquisition unit configured to acquire motion data of a user;

the processing unit is used for determining a motion curve of the user according to the motion data; the motion curve is used for indicating a motion track of the user in the motion process and a motion stimulation degree in the motion process;

and the display unit is used for displaying the motion curve.

13. The apparatus of claim 12,

the processing unit is used for respectively determining a motion track curve and a motion stimulation degree curve according to the motion data; determining the motion curve according to the motion trail curve and the motion stimulation degree curve; the motion trail curve is used for representing a motion trail of the user in a motion process, and the motion stimulation degree curve is used for representing a corresponding relation between time and motion stimulation degree in the motion process.

14. The apparatus of claim 13,

the processing unit is used for determining colors corresponding to the exercise stimulation degrees of at least two first time points in the exercise stimulation degree curve according to the mapping relation between the exercise stimulation degrees and the colors; and modifying the color corresponding to each first time point in the at least two first time points in the motion trail curve into the color corresponding to the motion stimulation degree of the first time point to obtain the motion curve.

15. The apparatus of claim 13 or 14,

the processing unit is specifically configured to determine an exercise item corresponding to the exercise data based on a big data analysis technology; and determining the motion trail curve according to the motion item.

16. The apparatus of claim 15,

the processing unit is specifically configured to determine a preset motion trajectory curve corresponding to the motion item as the motion trajectory curve if the motion trajectory corresponding to the motion item belongs to a first type; the motion items belonging to the first type correspond to respective motion trail curves; if the motion trail corresponding to the motion item belongs to a second type, fitting the motion data by adopting a curve fitting technology to obtain a motion trail curve; wherein the complexity of the motion trajectory of the motion item belonging to the second type is higher than the complexity of the motion trajectory of the motion item belonging to the first type.

17. The apparatus of any of claims 13-16, wherein the motion data comprises at least one of a motion speed, a motion acceleration, a motion altitude, or a heart rate;

the processing unit is specifically configured to determine a motion stimulation degree at each second time point in the at least two time points in the motion process according to the motion data at the second time point; and fitting the exercise stimulation degrees of the at least two time points by adopting a polynomial fitting technology to obtain the exercise stimulation degree curve.

18. The apparatus of claim 17, wherein the motion data comprises motion speed, motion acceleration, motion altitude, and heart rate;

the processing unit is specifically configured to perform normalization processing on the motion speed, the motion acceleration, the motion height and the heart rate corresponding to the second time point respectively to obtain the processed motion speed, motion acceleration, motion height and heart rate; and determining the exercise stimulation degree of the second time point according to the processed exercise speed, the exercise acceleration, the exercise height and the heart rate.

19. The apparatus of any one of claims 13-18,

the display unit is also used for displaying key information in the motion process; wherein the key information comprises at least one of a maximum movement height, a maximum movement speed, a maximum heart rate, or a motor stimulation level score.

20. The apparatus of claim 19,

the processing unit is further used for integrating the exercise stimulation degree curve to obtain an integration result; determining the exercise stimulation degree score according to the ratio of the integration result to the duration time of the exercise process; wherein the duration of the motion process is determined according to an end time and a start time of the motion process.

21. The apparatus of claim 20,

the processing unit is specifically configured to normalize the ratio to obtain a normalized ratio; and processing the normalized ratio based on a preset scoring interval to obtain the exercise stimulation degree score.

22. The apparatus according to any of claims 12-21, wherein the movement data comprises a movement speed and a movement altitude, and the processing unit is further configured to determine that the user enters a movement state if the movement speed is greater than a first speed threshold and/or the movement altitude is greater than a first altitude threshold; and if the movement speed is smaller than a second speed threshold value and the movement height is smaller than a second height threshold value, determining that the user exits the movement state.

23. A communication apparatus, characterized in that the apparatus comprises a processor and a memory, the memory having stored therein a computer program, the processor executing the computer program stored in the memory to cause the apparatus to execute the processing method of motion data according to any one of claims 1 to 11.

24. A communications apparatus, comprising: a processor and an interface circuit;

the interface circuit is used for receiving code instructions and transmitting the code instructions to the processor;

the processor is used for executing the code instructions to execute the processing method of the motion data according to any one of claims 1 to 11.

25. A readable storage medium characterized by storing instructions that, when executed, cause the method of processing motion data of any one of claims 1 to 11 to be implemented.